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2nd International Conference on Technology Enhanced Learning in Higher education, TELE 2022

作者： Kramarov, Sergey Olegovich Khramov, Vladimir Viktorovich Grebenyuk, Elena Vladamirovna Southern Federal University. SFU Department of Technology and Professional and Pedagogical Education Rostov-on-Don Russia Department of Computer Science Rostov-on-Don Russia Surgut State University SurSU Department of Automated Information Processing and Management Systems Surgut Russia

ISBN: (纸本)9781665470070

The theoretical and practical issues of the development of a control system for the technical vision of robots, which is based on the specifics of the processed information and the principles of building an integral system for understanding the image of the scene in its study, as well as the principles of building an integral system for recognizing the image of the scene, are considered. The issues of visual representation of objects and processes on rectangular, triangular and hexagonal rasters are considered. The results of these studies make it possible to analyze data presented through visual coding, intelligent processing and use for support and decision-making systems in a wide variety of fields of knowledge. The use of the proposed approach to the description of objects provides rapid analysis of raster images, prepares the initial information for more subtle studies, allows to quickly determine the important characteristics of the object in the image. © 2022 IEEE.

关键词： Object recognition

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Aspect-Based Sentiment Analysis Based on Knowledge Enhancement and Pre-trained Language Model

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第2期10卷 1168-1181页

作者： Ye, Lizhu Johar, Md Gapar Md Alkawaz, Mohammed Hazim School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou511363 China School of Graduate Studies Management and Science University Selangor Shah Alam40100 Malaysia Software Engineering and Digital Innovation Center Management and Science University Selangor Shah Alam40100 Malaysia Department of Computer Science College of Education for Pure Science University of Mosul Mosul41002 Iraq

Aspect-Based Sentiment Analysis (ABSA) is a significant task in the field of natural language processing, and the existing ABSA models not only lack the mining of sentiment knowledge, but also ignore the semantic feature representation of text. To address the above issues, this article designs an ABSA method relied on knowledge enhancement and Pre-trained Language Model (PLM). Firstly, a syntactic dependency graph is constructed based on the sentiment knowledge of the text to integrate the syntactic and sentiment information in the sentence. On this basis, PLM is utilized to encode words and sentences in the text context to enrich the semantic feature representation of the text. Secondly, the dependency graph of the sentence is enhanced by using emotional knowledge and the dependency types between words, and the node features are modeled by graph convolutional network to get the specific aspectual representation enhanced by emotional knowledge;meanwhile, the conceptual graph is enhanced by using conceptual graph for the aspectual word concepts and then fused with the specific aspectual representation to get the aspectual representation enhanced by multi-source knowledge. Then designing a dual interaction attention mechanism to realize the fusion of context and aspect features. Finally, the fused characteristic information is adopted as the input of the fully connected layer, and the softmax function is used to output the final sentiment polarity classification. The experimental outcome indicates that the suggested method outperforms the comparison model on the Restaurant and Twitter datasets, and better experimental outcome are obtained. © 2025 ISSN.

关键词： Emotion Recognition

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ADHERENT-Net: An Adaptive Health Risk Estimation Network for Real-Time Environmental Tracking

ADHERENT-Net: An Adaptive Health Risk Estimation Network for...

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Pervasive Computational technologies (ICPCT), International Conference on

作者： M. Priyadharshini V. Murugesh Subrata Chowdhury Bibhuti Bhusan Dash Utpal Chandra De Sudhansu Shekhar Patra Department of Computer Science and Engineering Faculty of Science and Technology(Icfai Tech) ICFAI Foundations for Higer Education Hyderabad India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of CSE Sreenivasa Institute of Technology and Management Studies Chittoor Anthra Pradesh India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Air borne pollutants greatly affect people’s well-being, so more accurate predictions are essential for the assessment of urban risks. Adaptive Health Risk Estimation Network is proposed in this paper as ADHERENT-Net to predict immediate health risks from environmental data. The model uses LSTM networks for temporal patterns discovery and Dense Neural Network for general feature learning and an additional meta-learner that combines both branches for an enhanced prediction capability. This one is prevented by a particular choice of its loss function where underestimations of threats to health are punished severely enough to prompt timely actions. ADHERENT-Net has a good predictive accuracy: the coefficient of determination, R 2 , is equal to 0,90 and the mean absolute error (MAE) is equal to 0,15. The dual branch structure is capable of handling time-dependent and non-time-dependent data from each other and the meta-learner also increases flexibility in changing conditions such as air quality, and seasonality. Training employed miscellaneous environmental data set with the pre-processing of data completeness and normalized data. ADHERENT-Net creates a reliable foundation for real-time health risk prediction, which has substantial potential to enhance the efficiency of program interventions in urban areas. Future work will concern tuning of the elaborated model parameters, the validation in various scenarios, and the generalization of the framework in other healthcare settings.

关键词： Adaptation models Accuracy Adaptive systems Urban areas Neural networks Estimation Water quality Predictive models Real-time systems Long short term memory

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The Effects of the Badminton Teaching-Assisted System Using Electromyography and Gyroscope on Learners' Badminton Skills

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IEEE Transactions on Learning technologies 2023年第5期16卷 780-789页

作者： Lin, Kuo-Chin Cheng, I-Ling Huang, Yin-Cheng Wei, Chun-Wang Chang, Wei-Lun Huang, Chenhsuan Chen, Nian-Shing National Sun Yat-sen University Center for Physical and Health Education Kaohsiung80424 Taiwan National Chung Hsing University Graduate Institute of Library and Information Science Taichung40227 Taiwan National Sun Yat-sen University Department of Information Management Kaohsiung80424 Taiwan Kaohsiung Medical University Hospital Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Department of Medical Research Kaohsiung80708 Taiwan National Taiwan Normal University Department of Graduate Institute of Information and Computer Education Taipei106209 Taiwan National Yunlin University of Science and Technology Department of Applied Foreign Languages Yunlin64002 Taiwan

Swing movements and muscle strength are essential for mastering badminton techniques. Traditionally, students learn badminton through their instructor's physical demonstration, verbal instructions, and small group activities. To enhance students' learning experience and assist badminton instructors more effectively, this study proposes an alternative teaching approach that utilizes a Badminton Teaching-Assisted System (BTAS) to teach swing movements. The system uses a gesture control armband to collect electromyography and gyroscope signals from professional players, which are then used to build a machine learning model based on the back propagation neural network. The model can be used as a scoring tool to assess the correctness of the student's swing movements and muscle strength. A total of 87 students participated in an eleven-week experiment focusing on two fundamental badminton skills: The backhand push and backhand lift. The results demonstrated a significant improvement in the correctness of the muscle strength during swing movements for the experimental group, compared to the control group. The BTAS can help to enhance the effectiveness of badminton instruction, thereby improving students' swing techniques. Future research could expand upon this study to develop more precise artificial intelligence-based sports assistance systems, which would facilitate personalized teaching and learning in physical education. © 2008-2011 IEEE.

关键词： Muscle

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An Improved Particle Swarm Approach for Energy-Aware Location-Aided Routing in Mobile Ad-Hoc Network

An Improved Particle Swarm Approach for Energy-Aware Locatio...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sandeep Kumar Khemraj Sharma P. M. Ashok Kumar Arpit Jain Sukesh Kumar Bhagat Pranita Singh Tula’s institute Dehradun U.K India Department of Business Administration Doctoral Program in Management Sciences Nanhua University Dalin Chiayi R.O.C Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science & Engineering Uttaranchal University Dehradun Uttarakhand India Department of Computer Science and Engineering Roorkee Institute of Technology Roorkee Uttarakhand India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

A network with no infrastructure, multiple hops, and dispersed nodes called a MANET. Nodes can communicate directly or through intermediaries without the need for permanent infrastructure. In dynamic networks like MANETs, routing procedures enhance the overall appearance of the network by ensuring Quality of Service (QoS). A routing protocol may use a composite routing measure to determine the best path for transferring data. Optimal location-based routing for MANETs based on energy-awareness is presented in this paper. Optimal routes are selected using this fast algorithm. Particle swarm optimization (PSO) is used instead of location-aided routing to select the normal data transmission route. Last but not least, the network is tested by simulating its packet delivery ratio, its energy level, its delay, and its overhead.

关键词： Scalability Transportation Quality of service Routing Particle measurements Routing protocols Real-time systems Delays Security Particle swarm optimization

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FedEnv-Net: Federated Neural Network for Decentralized Health Risk Prediction in Dynamic Urban Environments

FedEnv-Net: Federated Neural Network for Decentralized Healt...

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Pervasive Computational technologies (ICPCT), International Conference on

作者： M. Priyadharshini V. Murugesh Subrata Chowdhury Bibhuti Bhusan Dash Utpal Chandra De Sudhansu Shekhar Patra Department of Computer Science and Engineering Faculty of Science and Technology(Icfai Tech) ICFAI Foundations for Higher Education Hyderabad India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of CSE Sreenivasa Institute of Technology and Management Studies Chittoor Anthra Pradesh India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The predictions of health risks in urban settings are constrained by factors such as pollution and temperature which change over time. Centralized models have problems with privacy and scalability that often do not allow implementing solutions in real-world large, and distributed systems. To this end, we introduce the FedEnv-Net which is a federated neural network to predict health risk in a decentralized manner. Using federated learning, FedEnv-Net train’s models directly on edge devices, where data is trained and only updates are shared. Dynamics of temporal patterns and features are predicted by LSTM branch and Dense branch respectively in the model. A Meta-Learner merges such branches for prediction and a tuned loss function for conservative health risk estimations. Although it ensures privacy of data, FedEnv-Net yields an R 2 score of 0.92 and an MAE of 0.14, which determines its predictive capability. Future work includes increasing the efficiency of the communication between the system components, broadening the number of input data sources and improving the level of privacy to make FedEnv-Net a versatile, privacy-preserving solution for real-time health risk assessment.

关键词： Privacy Federated learning Neural networks Urban areas Predictive models Real-time systems Data models Risk management Long short term memory Context modeling

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An Improved Analysis of Weed Prediction in Precision Agriculture using Deep Learning

An Improved Analysis of Weed Prediction in Precision Agricul...

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Communication, computer sciences and Engineering (IC3SE), International Conference on

作者： Atul Kumar Pal B.D.K Patro Preeti Chaudhary Richa Anuj Kumar Navin Garg Department of Computer Science and Engineering Rajkiya Engineering College Kannauj Affiliated to Dr. A.P.J. AKTU Lucknow India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Information Technology Management Education & Research Institute (MERI) Affiliated to GGSIP University New Delhi India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The proliferation of weeds is a formidable obstacle for farmers in agriculture, resulting in diminished crop productivity and elevated expenses associated with their management. To tackle this problem, precision agricultural methods have been created to detect and control weeds promptly and effectively precisely. Deep learning is utilized to train a convolutional neural network (CNN) to identify patterns and characteristics in photographs of crop fields, enabling it to differentiate between weeds and crops. At first, drones or other airborne photography technologies will be used to gather high-resolution pictures of the crop fields. The photos are subsequently subjected to pre-processing techniques to eliminate noise and improve the characteristics of the crops and weeds. Afterward, the images are inputted into the model, which has several layers of artificial neurons that acquire the ability to extract characteristics and categorize the input images. During the training phase, the CNN utilizes a substantial dataset of labelled photos to adjust its parameters and enhance its precision in differentiating weeds and crops. After the model has been trained, it may be utilized to accurately forecast the existence of weeds in novel photographs of agricultural areas. Farmers may effectively identify and focus on specific regions for weed management by using this technology, decreasing the number of herbicides required and mitigating any ecological damage.

关键词： Deep learning Precision agriculture Training Productivity Accuracy Biological system modeling Crops

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Progress in Automated Cancer Detection with Recursive Neural Networks

Progress in Automated Cancer Detection with Recursive Neural...

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Communication, computer sciences and Engineering (IC3SE), International Conference on

作者： Richa B.D.K Patro Mukesh Kumar Atul Pal Anuj Kumar Manisha Aeri Department of Computer Science and Engineering Rajkiya Engineering College Kannauj Affiliated to Dr. A.P.J. AKTU Lucknow India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Information Technology Management Education & Research Institute (MERI) Affiliated to GGSIP University New Delhi India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Detecting cancer is a crucial step in the prognosis and treatment of most cancers and consequently, there has been significant progress in the development of automatic strategies for detecting cancer. One approach that has shown promising results in this area is the usage of Recursive Neural Networks (RNNs). RNNs are a sort of artificial neural network that has the capacity to method sequential statistics, making them appropriate for reading clinical pictures, which include those used in cancer detection. Using RNNs in cancer detection has advanced extensively in recent years, with several researches demonstrating their potential to appropriately detect most cancers with various sorts of clinical images. One observer applied RNNs to research breast tissue images and completed an accuracy of 98.6%, outperforming conventional gadget learning algorithms. Some other examiner used RNNs to research colonoscopy pictures and detected most cancers with an accuracy of 96.6%, surpassing the performance of each human and traditional gadget mastering tactics. One of the key benefits of the use of RNNs in cancer detection is their capability to analyze from statistics and make selections based on patterns and relationships in the records, without the need for characteristic engineering or manual annotation. This makes them nicely-applicable for analyzing complicated scientific photographs wherein traditional methods may lag. In most cancers detection, short and accurate diagnosis is critical for timely treatment and enhancing effects. Therefore, the performance of RNN-based models is a vital aspect in their overall performance analysis.

关键词： Recurrent neural networks Accuracy Shape Artificial neural networks Observers Cancer detection Performance analysis

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Energy Consumption Prediction for Home Appliances with Recurrent Neural Networks 4

Energy Consumption Prediction for Home Appliances with Recur...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Aancy, H. Mickle Mary, S. Anitha Janet Manikumar, T. Saravanan, G. Mohanavel, V. Gupta, Rahul Dev Panimalar Engineering College Department of Management Studies Tamil Nadu Chennai600123 India Noorul Islam Centre for Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Kumaracoil629180 India Kalasalingam Academy of Research and Education Deemed to be University Department of Computer Science and Engineering Tamil Nadu 626126 India KPR Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641407 India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Tamil Nadu Chennai600073 India Department of Mechanical Engineering Haryana 133207 India

ISBN: (纸本)9798350385793

Efficient energy management requires a thorough understanding of individual appliance energy consumption in both residential and business environments. Because of the growing number of Internet of Things (loT) devices in smart homes, household appliances are a significant source of total energy consumption. Commonly used equipment such as heating systems, washing machines, and vacuum cleaners have a substantial impact on daily energy consumption. Significant energy savings can only be realized by careful management and forecasting of these appliances' energy consumption. This research employs Recurrent Neural Network (RNN) models for energy forecasting of housing appliances, with Long Short-Term Memory (LSTM) and a hybrid model known as Bi-LSTM-Attention Network. A Kaggle dataset with 28 features is used in this study, which is then reduced to 17 using a correlation plot. The processed data is used in both the training and testing phases of the RNN models. Many metrics are used to evaluate the performance of the models, including R-squared (R2), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The Bi- LSTM-Attention Network achieves excellent performance, with training and testing R2 values of 0.9978 and 0.9835, respectively. In addition, lower MSE and RMSE values demonstrate that Bi-LSTM-Attention Network constantly outperforms LSTM. The findings show that the proposed Bi-LSTM-Attention Network is capable of accurately predicting the amount of energy household appliances will consume. © 2024 IEEE.

关键词： Long short-term memory

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Resnet Based Blockchain Architecture for The Detection of Plant Leaf Disease in Agriculture Field

Resnet Based Blockchain Architecture for The Detection of Pl...

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2023 International Conference on Disruptive technologies, ICDT 2023

作者： Aruna Devi, B. Praveen Kumar, M. Maguluri, Lakshmana Phaneendra Tamilselvan, P. Dr.NGP Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Sri Eshwar College of Engineering Department of Cse Tamil Nadu Coimbatore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Guntur India Mahendra Engineering College Department of Management Studies Tamil Nadu Namakkal India

ISBN: (纸本)9798350323887

The only way to get better crop yields is to find and treat crop diseases quickly. Deep learning models diagnoses the plant diseases by looking at the leaves. A residual neural network is developed for the detection of disease in maize leaf. The leaves are collected from the available dataset, where the detection architecture is decentralized using blockchain architecture. The residual neural network with decentralized blockchain enables an optimal classification of instances. The model is implemented with improved disease detection accuracy with reduced training time in a python simulator with keras library. The results of simulation show an improved rate of classification accuracy, precision, recall land f-measure in detecting the leaf disease than the existing convolutional neural network models. © 2023 IEEE.

关键词： Diagnosis

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